Murray, Iain

34 publications

AAAI 2021 CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-Cloud Stream Forecasting Chaoyun Zhang, Marco Fiore, Iain Murray, Paul Patras

ICLRW 2021 Lossless Compression with State Space Models Using Bits Back Coding James Townsend, Iain Murray

NeurIPS 2021 Maximum Likelihood Training of Score-Based Diffusion Models Yang Song, Conor Durkan, Iain Murray, Stefano Ermon

ICML 2020 On Contrastive Learning for Likelihood-Free Inference Conor Durkan, Iain Murray, George Papamakarios

ICML 2019 BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning Asa Cooper Stickland, Iain Murray

ICLR 2019 Mode Normalization Lucas Deecke, Iain Murray, Hakan Bilen

NeurIPS 2019 Neural Spline Flows Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios

AISTATS 2019 Sequential Neural Likelihood: Fast Likelihood-Free Inference with Autoregressive Flows George Papamakarios, David Sterratt, Iain Murray

ICML 2018 Dynamic Evaluation of Neural Sequence Models Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals

AISTATS 2017 Markov Chain Truncation for Doubly-Intractable Inference Colin Wei, Iain Murray

NeurIPS 2017 Masked Autoregressive Flow for Density Estimation George Papamakarios, Theo Pavlakou, Iain Murray

ICLR 2017 Multiplicative LSTM for Sequence Modelling Ben Krause, Iain Murray, Steve Renals, Liang Lu

NeurIPS 2016 Fast Ε-Free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios, Iain Murray

JMLR 2016 Neural Autoregressive Distribution Estimation Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle

AISTATS 2016 Pseudo-Marginal Slice Sampling Iain Murray, Matthew M. Graham

ICML 2015 MADE: Masked Autoencoder for Distribution Estimation Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle

ICML 2014 A Deep and Tractable Density Estimator Benigno Uria, Iain Murray, Hugo Larochelle

JMLR 2014 Parallel MCMC with Generalized Elliptical Slice Sampling Robert Nishihara, Iain Murray, Ryan P. Adams

JMLR 2013 A Framework for Evaluating Approximation Methods for Gaussian Process Regression Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray

NeurIPS 2013 RNADE: The Real-Valued Neural Autoregressive Density-Estimator Benigno Uria, Iain Murray, Hugo Larochelle

NeurIPS 2011 How Biased Are Maximum Entropy Models? Jakob H. Macke, Iain Murray, Peter E. Latham

AISTATS 2011 The Neural Autoregressive Distribution Estimator Hugo Larochelle, Iain Murray

AISTATS 2010 Elliptical Slice Sampling Iain Murray, Ryan Adams, David MacKay

UAI 2010 Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes Ryan Prescott Adams, George E. Dahl, Iain Murray

NeurIPS 2010 Slice Sampling Covariance Hyperparameters of Latent Gaussian Models Iain Murray, Ryan P. Adams

ICML 2009 Evaluation Methods for Topic Models Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, David M. Mimno

ICML 2009 Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities Ryan Prescott Adams, Iain Murray, David J. C. MacKay

NeurIPS 2008 Characterizing Response Behavior in Multisensory Perception with Conflicting Cues Rama Natarajan, Iain Murray, Ladan Shams, Richard S. Zemel

NeurIPS 2008 Evaluating Probabilities Under High-Dimensional Latent Variable Models Iain Murray, Ruslan Salakhutdinov

ICML 2008 On the Quantitative Analysis of Deep Belief Networks Ruslan Salakhutdinov, Iain Murray

NeurIPS 2008 The Gaussian Process Density Sampler Iain Murray, David MacKay, Ryan P. Adams

UAI 2006 MCMC for Doubly-Intractable Distributions Iain Murray, Zoubin Ghahramani, David J. C. MacKay

NeurIPS 2005 Nested Sampling for Potts Models Iain Murray, David MacKay, Zoubin Ghahramani, John Skilling

UAI 2004 Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms Iain Murray, Zoubin Ghahramani